• Act as a technical advisor for the integration between Databricks and data privacy/governance tools. • Support the configuration of connectivity, access permissions, metadata, and security requirements within Databricks. • Guide and validate data discovery, classification, and scanning processes across the Data Lake. • Provide expertise on Databricks data structures, access patterns, and security models. • Collaborate with engineering and business teams to ensure data integrity, usability, and compliance. • Document data architectures, workflows, and technical processes. • Support troubleshooting and provide technical guidance on data-related challenges. • Contribute to Data Lake and Data Warehouse architecture discussions and best practices.

• Drive architectural strategy discussions with client leadership to ensure data platform initiatives align with business objectives and deliver measurable value • Act as the senior technical voice in client engagements, translating complex architectural decisions into business outcomes for both technical and executive audiences • Manage client relationships from an architectural perspective, building trust through technical excellence and strategic insight • Lead cross-functional teams of data engineers, data scientists, and analytics specialists in designing and deploying scalable, cloud-native data platforms • Support teams in planning and execution, providing architectural guidance and removing technical blockers throughout the delivery lifecycle • Mentor team members on cloud architecture best practices, data modeling principles, and emerging technologies • Design and implement robust AWS-based data lake architectures using Medallion (Bronze/Silver/Gold) patterns, managing trade-offs in storage strategies, partitioning schemes, and schema evolution • Lead the transition of legacy data structures and systems to modern cloud platforms, developing migration strategies that minimize risk and maximize business continuity • Architect solutions that balance performance, cost, scalability, and maintainability across complex data ecosystems • Partner with Data & Analytics Managers and business stakeholders to translate business requirements into feasible, scalable architectural decisions • Define data models, integration patterns, and platform capabilities that directly support business use cases and analytics needs • Navigate complex, siloed data landscapes to design practical solutions that deliver value incrementally • Design data platforms that natively support AI and ML workloads, including feature engineering pipelines, feature stores, model training data preparation, and inference serving infrastructure • Architect MLOps capabilities as a core platform component, not an afterthought, ensuring seamless integration of machine learning lifecycle management • Implement solutions leveraging agentic AI technologies to optimize data management, automate data quality workflows, and enhance analytics capabilities • Advise on governance models (centralized vs. federated) appropriate to organizational structure and data maturity • Implement Data Mesh principles to enable domain-oriented ownership while maintaining platform-level standards and interoperability • Define data quality frameworks, metadata management strategies, and security/privacy controls that scale across distributed architectures • Integrate AI tools and methodologies into daily architectural work, demonstrating innovative approaches to design, documentation, and problem-solving • Stay current with emerging data and AI technologies, evaluating their applicability to client challenges and incorporating them into platform strategies

• Serve as a trusted technical advisor for clients, bridging business strategy and cloud architecture • Lead the design and implementation of cloud-native data architectures with an emphasis on AWS solutions • Manage client relationships, building trust through technical excellence and strategic insight • Lead cross-functional teams in designing and deploying scalable, cloud-native data platforms • Support teams during execution, providing architectural guidance and removing blockers • Mentor team members on cloud architecture best practices and technologies • Design and implement robust AWS-based data lake architectures • Transition legacy data systems to modern cloud platforms, developing migration strategies • Partner with stakeholders to translate business requirements into architectural decisions • Design platforms that support AI/ML workloads and integrate AI tools into architectural work

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery • Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features • Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly • Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams • Improve data quality and reliability through testing, observability, alerting, and validation frameworks • Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions • Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions